Checking the weather? Looking at a map of the world to plan your next vacation? Guess what—you’re using a model. While models can be useful for gaining insights that can help us make good decisions, they are simplifications of reality.

One example of a model is a weather forecast. Using data on current and past weather conditions, a meteorologist makes a number of assumptions and attempts to approximate what the weather will be in the future. This model may help you decide if you should bring an umbrella when you leave the house in the morning. However, as anyone who has been caught without an umbrella in an unexpected rain shower knows, reality often behaves differently than a model predicts it will.

In investment management, models are used by investors to gain insights that can help inform investment decisions. Financial researchers are frequently looking for new models to help answer questions like “What drives returns?” These models are often touted as being complex and sophisticated and incite debates about who has a “better” model. Investors who are evaluating investment strategies can benefit from understanding that the reality of markets, just like the weather, cannot be fully explained by any model. Hence, investors should be wary of any approach that requires a high degree of trust in a model alone.

THE MODEL, THE USER, AND THE APPLICATION

Just like with the weather forecasts, investment models rely on different inputs. Instead of things like barometric pressure or wind conditions, investment models may look at variables like the expected return or volatility of different securities. For example, using these sorts of inputs, one type of investment model may recommend an “optimal” mix of securities based on how these characteristics are expected to interact with one another over time. Users should be cautious though. The saying “garbage in, garbage out” applies to models and their inputs. In other words, a model’s output can only be as good as its input. Poor assumptions can lead to poor recommendations. However, even with sound underlying assumptions, a user who places too much faith in inherently imprecise inputs can still be exposed to extreme outcomes.

Nobel laureate Robert Merton offered some useful insights on this topic in an interview with David Booth, Chairman and Co-CEO of Dimensional Fund Advisors. “You’ll often hear people say, during the [financial] crisis or something, ‘There were bad models and good models.’ And someone will say, ‘Is yours a good model?’ That sounds like a good question, a reasonable question. But, actually, it isn’t really well-posed. You need a triplet: a model, the user of the model, and its application. You cannot judge a model in the abstract.” (For a video of the interview, please click the following link: Models Interview.)

“THE EARTH IS ROUND,” INVESTING, AND THE JUDGMENT GAP"

Consider the shape of the earth. One simple modeldescribes the earth as a round sphere. While this is agood approximation, it is not completely accurate.In reality, the earth is an imperfect oblate spheroid—fatter at the equator and more squashed at the polesthan a perfect sphere. Additionally, the surface of theplanet is varied and changing: There are mountains, rivers,and valleys—it is not perfectly smooth. So how should wejudge the model of “the earth is round”? For a parentteaching their child about the solar system or for amanufacturer of globes, assuming the earth is a perfectsphere is likely a fine application of the model. For ageologist studying sea levels or NASA engineerslaunching an object into space, it is likely a poor model.The difference lies in the user of the model and its application.

In investing, one should pay similar attention to the detailsof user and application when a model informs real-worldinvestment decisions. For example, for investors in publicmarkets, the efficient market hypothesis (EMH) is auseful model stating that asset prices reflect all availableinformation. This model helps inform investors that theycan rely on prices and that it is not worth trying to outguessthe ones set collectively by millions of market participants.This insight has been confirmed by numerous studies oninvestment manager performance.[1] In applying this modelto real-world investment solutions, however, there areseveral nuances that a user must understand in order tobridge the gap between theory and practice. Even if pricesquickly reflect information, one should not assume that theEMH protects investors from making investment mistakes.Rigorous attention must be paid to trading costs and toavoid trading in situations when there may be asymmetricinformation or illiquidity that might disadvantage investors.To quote Professor Merton again, successful use of a modelis “10% inspiration and 90% perspiration.” In other words,having a good idea is just the beginning. Most of the effortis in implementing the idea and making it work. In the end,there is a difference between blindly following a model andusing it judiciously to guide your decisions. By employingsound judgment and thoughtful implementation, we believeit is more likely that outcomes will be consistent witha user’s expectations.

So what is an investor to do with this knowledge? Whenevaluating investment approaches, understanding amanager’s ability to effectively test and implement ideasgarnered from models into real-world applications is animportant first step. An investor who hires an investmentmanager to bridge this gap is placing trust in the judgmentof that manager. The transparency offered by someapproaches, such as traditional index funds, requires a lowlevel of trust because the model is quite simple and it is easyto evaluate whether or not they have matched the returnof the index. The tradeoff with this level of mechanicaltransparency is that it may sacrifice the potential for higherreturns, as it prioritizes matching the index over anythingelse. For more opaque and complex approaches, like manyactive or complex quantitative strategies, the requisite levelof trust required is much higher. Investors should look tounderstand how these managers use models and questionhow to evaluate the effectiveness of their implementation.

By selecting an investment manager that has experience in effectively putting financial research into practice and executing an approach that balances transparency with value-added implementation, investors should increase the probability of having a positive investment experience.

[1]. For example, see Fama and French (2010), “Luck vs. Skill in the Cross Section of Mutual Fund Returns.”

Article from Dimensional Fund Advisors LP.

Past performance is no guarantee of future results. There is no guarantee an investing strategy will be successful.

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